RegularizationĀ¶
Regularization is about preventing the model from "memorizing" our specific Santa images and forcing it to learn general features that define a "Santa" in any environment.
Now that we know our model and data pipeline are working OK, the next steps in the Karpathy Recipe are to re-enable regularization.
There are several elements to that:
With L2 regularization we add a penalty on large weights. It prevents overfitting. (
weight_decay=0.0005)Data augmentation increases data diversity and helps overcome limitations of our small dataset. Rotation, scaling, flipping, color adjustments and mosaic recombinations all help make the model more robust to real-life variations. (
augment=True)Dropout randomly deactivates a fraction of neurons during each training step, which prevents the network from relying too heavily on specific pathways and forces it to generalize. The principle behind dropout as regularization is adding some noise to the intermediate output of neurons during the training process.(
dropout=0.1)Early stopping: if the validation loss stops improving (or starts increasing) for a specified number of epochs, the training is stopped. (
patience=20)
InĀ [Ā ]:
## For when the Notebook hangs up
# drive.flush_and_unmount()
# !sudo killall apt apt-get 2>/dev/null
# !rm -rf /var/lib/dpkg/lock*
# !rm -rf /var/cache/apt/archives/lock
InĀ [1]:
from google.colab import drive
drive.mount('/content/drive/')
Mounted at /content/drive/
InĀ [2]:
# If the notebook is run from the original or from shared location:
%cd /content/drive/MyDrive/FHNW/HS_25/DLBS/minichallenge_hs25_object_detection/MC
# %cd /content/drive/MyDrive/FHNW/MC
from helpers import (
save_weights_to_drive,
visualize_training_results,
display_yolo_plots,
diagnose_training_results
)
/content/drive/MyDrive/FHNW/HS_25/DLBS/minichallenge_hs25_object_detection/MC
InĀ [Ā ]:
!nvidia-smi
Mon Jan 12 02:43:13 2026
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 550.54.15 Driver Version: 550.54.15 CUDA Version: 12.4 |
|-----------------------------------------+------------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+========================+======================|
| 0 NVIDIA A100-SXM4-40GB Off | 00000000:00:04.0 Off | 0 |
| N/A 31C P0 48W / 400W | 0MiB / 40960MiB | 0% Default |
| | | Disabled |
+-----------------------------------------+------------------------+----------------------+
+-----------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=========================================================================================|
| No running processes found |
+-----------------------------------------------------------------------------------------+
Install Dependencies & Authentication (Ultralitycs and Weights & Biases)Ā¶
InĀ [Ā ]:
# Install YOLO library
!pip install ultralytics -q
from IPython import display
display.clear_output()
# prevent ultralytics from tracking activity
!yolo settings sync=False
import ultralytics
ultralytics.checks()
from ultralytics import YOLO
Ultralytics 8.3.252 š Python-3.12.12 torch-2.9.0+cu126 CUDA:0 (NVIDIA A100-SXM4-40GB, 40507MiB) Setup complete ā (12 CPUs, 83.5 GB RAM, 38.5/112.6 GB disk)
InĀ [Ā ]:
# W&B login
!pip install wandb -q
import wandb
from google.colab import userdata
WANDB_API_KEY = userdata.get('WANDB_API_KEY')
wandb.login(key=WANDB_API_KEY)
/usr/local/lib/python3.12/dist-packages/notebook/notebookapp.py:191: SyntaxWarning: invalid escape sequence '\/' | |_| | '_ \/ _` / _` | _/ -_)
1. Load model and model metadata about the datasetsĀ¶
InĀ [Ā ]:
import os
import shutil
from ultralytics import YOLO
# --- LOAD MODEL FROM CACHE ---
DRIVE_PATH = '/content/drive/My Drive/FHNW/HS_25/DLBS/minichallenge_hs25_object_detection/MC/models_cache/'
MODEL_PATH = 'models_cache/yolov8n.pt' # Nano
os.makedirs(DRIVE_PATH, exist_ok=True)
if os.path.exists(MODEL_PATH):
print(f"ā
Loading from cache: {MODEL_PATH}")
model = YOLO(MODEL_PATH)
else:
print(f"ā¬ļø Downloading {MODEL_NAME}...")
model = YOLO(MODEL_NAME)
source = MODEL_NAME if os.path.exists(MODEL_NAME) else os.path.expanduser(f'~/.config/ultralytics/{MODEL_NAME}')
if os.path.exists(source):
shutil.copyfile(source, MODEL_PATH)
if source == MODEL_NAME:
os.remove(MODEL_NAME)
print(f"ā
Saved to Drive: {MODEL_PATH}")
# Set up data location
dataset_yaml = 'Santa-10/data.yaml'
ā Loading from cache: models_cache/yolov8n.pt
InĀ [Ā ]:
regularized_config_1 = {
'data': dataset_yaml,
'epochs': 180, # Limited epochs to prevent overfitting
'batch': 16, # Smaller batch size
'patience': 20, # Triggers early stopping after 20 epochs without improvement
'device': 0,
'augment': True, # Enable augmentation
'lr0': 0.01, # Initial learning rate (default is ~0.01)
'weight_decay': 0.005, # L2 regularization (default: 0.005)
'dropout': 0.1, # Dropout rate (0.0-0.5, applied in classification head)
'optimizer': 'Adam', # Or 'SGD', but Adam better for small datasets
'verbose': True,
'save': True,
'project': 'runs/regularize',
'name': 'regularize_run_1',
'val': True,
'exist_ok': True,
'freeze': None # No frozen layers (all layers trainable)
}
About the chosen parameters:Ā¶
By choosing YOLO Nano over Small we are trying to trade trading off model capacity for better generalization.Because it has fewer weights, it is physically harder for the model to "memorize" every pixel of our images .It has less storage space to memorize noise and specific backgrounds, and thus worksd as a structural regularizer.
180 epochs is a safer ceiling than 250 to not overfit.
If the Validation mAP stops improving for 20 epochs,
patience=20kills the run.Dropout 0.1 means that during training 10% of neurons are randomly deactivated, which forces the classification head not to rely on any single set of neurons.
weight_decayis L2 penalty that keeps weights small. Default is 0.0005, but it appears to be too small for our data.
Train Models with RegularizationĀ¶
Run 1: Nano YOLOĀ¶
InĀ [Ā ]:
# YOLO nano
model = YOLO(MODEL_PATH)
InĀ [Ā ]:
# # --- RUN 1
# --- TRAIN ---
print("š Starting training (designed NOT to overfit) ...")
results = model.train(**regularized_config_1)
š Starting training (designed NOT to overfit) ... Ultralytics 8.3.252 š Python-3.12.12 torch-2.9.0+cu126 CUDA:0 (NVIDIA H100 80GB HBM3, 81224MiB) engine/trainer: agnostic_nms=False, amp=True, augment=True, auto_augment=randaugment, batch=16, bgr=0.0, box=7.5, cache=False, cfg=None, classes=None, close_mosaic=10, cls=0.5, compile=False, conf=None, copy_paste=0.0, copy_paste_mode=flip, cos_lr=False, cutmix=0.0, data=Santa-10/data.yaml, degrees=0.0, deterministic=True, device=0, dfl=1.5, dnn=False, dropout=0.1, dynamic=False, embed=None, epochs=180, erasing=0.4, exist_ok=True, fliplr=0.5, flipud=0.0, format=torchscript, fraction=1.0, freeze=None, half=False, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, imgsz=640, int8=False, iou=0.7, keras=False, kobj=1.0, line_width=None, lr0=0.01, lrf=0.01, mask_ratio=4, max_det=300, mixup=0.0, mode=train, model=models_cache/yolov8n.pt, momentum=0.937, mosaic=1.0, multi_scale=False, name=regularize_run_1, nbs=64, nms=False, opset=None, optimize=False, optimizer=Adam, overlap_mask=True, patience=20, perspective=0.0, plots=True, pose=12.0, pretrained=True, profile=False, project=runs/regularize, rect=False, resume=False, retina_masks=False, save=True, save_conf=False, save_crop=False, save_dir=/content/drive/MyDrive/FHNW/HS_25/DLBS/minichallenge_hs25_object_detection/MC/runs/detect/runs/regularize/regularize_run_1, save_frames=False, save_json=False, save_period=-1, save_txt=False, scale=0.5, seed=0, shear=0.0, show=False, show_boxes=True, show_conf=True, show_labels=True, simplify=True, single_cls=False, source=None, split=val, stream_buffer=False, task=detect, time=None, tracker=botsort.yaml, translate=0.1, val=True, verbose=True, vid_stride=1, visualize=False, warmup_bias_lr=0.1, warmup_epochs=3.0, warmup_momentum=0.8, weight_decay=0.0005, workers=8, workspace=None Downloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf': 100% āāāāāāāāāāāā 755.1KB 36.9MB/s 0.0s Overriding model.yaml nc=80 with nc=1 from n params module arguments 0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2] 1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2] 2 -1 1 7360 ultralytics.nn.modules.block.C2f [32, 32, 1, True] 3 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2] 4 -1 2 49664 ultralytics.nn.modules.block.C2f [64, 64, 2, True] 5 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2] 6 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True] 7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2] 8 -1 1 460288 ultralytics.nn.modules.block.C2f [256, 256, 1, True] 9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5] 10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] 12 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1] 13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] 15 -1 1 37248 ultralytics.nn.modules.block.C2f [192, 64, 1] 16 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] 17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1] 18 -1 1 123648 ultralytics.nn.modules.block.C2f [192, 128, 1] 19 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] 20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1] 21 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1] 22 [15, 18, 21] 1 751507 ultralytics.nn.modules.head.Detect [1, [64, 128, 256]] Model summary: 129 layers, 3,011,043 parameters, 3,011,027 gradients, 8.2 GFLOPs Transferred 319/355 items from pretrained weights Freezing layer 'model.22.dfl.conv.weight' AMP: running Automatic Mixed Precision (AMP) checks... AMP: checks passed ā train: Fast image access ā (ping: 0.4Ā±0.1 ms, read: 0.1Ā±0.0 MB/s, size: 32.3 KB) train: Scanning /content/drive/MyDrive/FHNW/HS_25/DLBS/minichallenge_hs25_object_detection/MC/Santa-10/train/labels.cache... 700 images, 327 backgrounds, 0 corrupt: 100% āāāāāāāāāāāā 700/700 154.5Mit/s 0.0s albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8)) val: Fast image access ā (ping: 0.4Ā±0.1 ms, read: 0.1Ā±0.0 MB/s, size: 31.2 KB) val: Scanning /content/drive/MyDrive/FHNW/HS_25/DLBS/minichallenge_hs25_object_detection/MC/Santa-10/valid/labels.cache... 155 images, 54 backgrounds, 0 corrupt: 100% āāāāāāāāāāāā 155/155 12.7Mit/s 0.0s Plotting labels to /content/drive/MyDrive/FHNW/HS_25/DLBS/minichallenge_hs25_object_detection/MC/runs/detect/runs/regularize/regularize_run_1/labels.jpg... optimizer: Adam(lr=0.01, momentum=0.937) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias(decay=0.0) Image sizes 640 train, 640 val Using 8 dataloader workers Logging results to /content/drive/MyDrive/FHNW/HS_25/DLBS/minichallenge_hs25_object_detection/MC/runs/detect/runs/regularize/regularize_run_1 Starting training for 180 epochs... Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 1/180 2.14G 1.795 2.728 2.109 16 640: 100% āāāāāāāāāāāā 44/44 3.2it/s 13.8s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 4.3s/it 21.6s all 155 105 0.00407 0.781 0.00403 0.00133 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 2/180 2.62G 2.085 2.719 2.384 22 640: 100% āāāāāāāāāāāā 44/44 19.5it/s 2.3s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 15.8it/s 0.3s all 155 105 0 0 0 0 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 3/180 2.64G 2.015 2.677 2.329 16 640: 100% āāāāāāāāāāāā 44/44 19.8it/s 2.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 16.2it/s 0.3s all 155 105 0.000891 0.152 0.00038 9.26e-05 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 4/180 2.64G 1.945 2.553 2.224 17 640: 100% āāāāāāāāāāāā 44/44 21.0it/s 2.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 17.7it/s 0.3s all 155 105 0.00121 0.0381 0.000411 0.0001 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 5/180 2.64G 1.925 2.467 2.192 22 640: 100% āāāāāāāāāāāā 44/44 20.2it/s 2.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 17.7it/s 0.3s all 155 105 0.0749 0.257 0.0459 0.0133 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 6/180 2.64G 1.818 2.369 2.131 19 640: 100% āāāāāāāāāāāā 44/44 21.1it/s 2.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 19.7it/s 0.3s all 155 105 0.117 0.0476 0.0219 0.00468 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 7/180 2.66G 1.907 2.38 2.155 14 640: 100% āāāāāāāāāāāā 44/44 20.5it/s 2.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 19.5it/s 0.3s all 155 105 0.0789 0.0952 0.0509 0.00956 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 8/180 2.67G 1.794 2.303 2.083 35 640: 100% āāāāāāāāāāāā 44/44 21.1it/s 2.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 18.7it/s 0.3s all 155 105 0.0385 0.162 0.0204 0.00431 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 9/180 2.67G 1.725 2.22 2.026 18 640: 100% āāāāāāāāāāāā 44/44 21.3it/s 2.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 20.0it/s 0.3s all 155 105 0.141 0.124 0.128 0.0304 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 10/180 2.67G 1.669 2.15 1.964 22 640: 100% āāāāāāāāāāāā 44/44 21.9it/s 2.0s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 19.5it/s 0.3s all 155 105 0.119 0.276 0.0992 0.0313 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 11/180 2.69G 1.733 2.209 2.016 11 640: 100% āāāāāāāāāāāā 44/44 19.4it/s 2.3s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 19.7it/s 0.3s all 155 105 0.393 0.571 0.353 0.141 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 12/180 2.69G 1.764 2.198 2.022 20 640: 100% āāāāāāāāāāāā 44/44 20.6it/s 2.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 16.9it/s 0.3s all 155 105 0.376 0.298 0.261 0.0959 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 13/180 2.69G 1.587 2.088 1.904 12 640: 100% āāāāāāāāāāāā 44/44 21.2it/s 2.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 17.3it/s 0.3s all 155 105 0.312 0.257 0.196 0.073 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 14/180 2.69G 1.601 2.053 1.893 10 640: 100% āāāāāāāāāāāā 44/44 21.3it/s 2.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 19.4it/s 0.3s all 155 105 0.199 0.4 0.181 0.0508 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 15/180 2.69G 1.644 2.107 1.947 15 640: 100% āāāāāāāāāāāā 44/44 21.8it/s 2.0s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 19.0it/s 0.3s all 155 105 0.302 0.333 0.258 0.0862 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 16/180 2.69G 1.636 2.078 1.93 21 640: 100% āāāāāāāāāāāā 44/44 21.7it/s 2.0s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 18.5it/s 0.3s all 155 105 0.328 0.467 0.273 0.105 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 17/180 2.69G 1.587 1.995 1.883 16 640: 100% āāāāāāāāāāāā 44/44 20.6it/s 2.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 19.7it/s 0.3s all 155 105 0.306 0.486 0.313 0.117 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 18/180 2.69G 1.579 2.075 1.872 15 640: 100% āāāāāāāāāāāā 44/44 20.8it/s 2.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 21.4it/s 0.2s all 155 105 0.259 0.105 0.0804 0.0236 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 19/180 2.69G 1.557 2.025 1.848 15 640: 100% āāāāāāāāāāāā 44/44 20.9it/s 2.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 18.9it/s 0.3s all 155 105 0.354 0.271 0.294 0.0924 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 20/180 2.69G 1.545 1.951 1.857 16 640: 100% āāāāāāāāāāāā 44/44 21.8it/s 2.0s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 19.4it/s 0.3s all 155 105 0.432 0.514 0.36 0.132 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 21/180 2.69G 1.609 2.003 1.885 16 640: 100% āāāāāāāāāāāā 44/44 21.1it/s 2.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 19.1it/s 0.3s all 155 105 0.313 0.59 0.304 0.118 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 22/180 2.71G 1.562 1.943 1.832 20 640: 100% āāāāāāāāāāāā 44/44 21.2it/s 2.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 20.1it/s 0.2s all 155 105 0.45 0.61 0.507 0.222 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 23/180 2.71G 1.619 1.984 1.883 22 640: 100% āāāāāāāāāāāā 44/44 21.5it/s 2.0s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 19.8it/s 0.3s all 155 105 0.0818 0.343 0.0605 0.0165 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 24/180 2.71G 1.476 1.886 1.79 11 640: 100% āāāāāāāāāāāā 44/44 21.5it/s 2.0s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 20.6it/s 0.2s all 155 105 0.415 0.433 0.347 0.14 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 25/180 2.71G 1.58 1.908 1.842 16 640: 100% āāāāāāāāāāāā 44/44 20.4it/s 2.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 19.5it/s 0.3s all 155 105 0.346 0.624 0.326 0.126 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 26/180 2.71G 1.58 1.935 1.818 18 640: 100% āāāāāāāāāāāā 44/44 20.2it/s 2.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 15.6it/s 0.3s all 155 105 0.389 0.63 0.443 0.198 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 27/180 2.71G 1.435 1.801 1.768 17 640: 100% āāāāāāāāāāāā 44/44 19.9it/s 2.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 18.1it/s 0.3s all 155 105 0.493 0.59 0.461 0.194 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 28/180 2.71G 1.47 1.904 1.786 14 640: 100% āāāāāāāāāāāā 44/44 20.8it/s 2.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 19.4it/s 0.3s all 155 105 0.469 0.438 0.379 0.138 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 29/180 2.71G 1.506 1.888 1.807 16 640: 100% āāāāāāāāāāāā 44/44 21.0it/s 2.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 20.1it/s 0.2s all 155 105 0.47 0.6 0.486 0.23 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 30/180 2.71G 1.471 1.799 1.764 19 640: 100% āāāāāāāāāāāā 44/44 20.8it/s 2.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 19.5it/s 0.3s all 155 105 0.514 0.638 0.541 0.285 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 31/180 2.71G 1.49 1.779 1.786 21 640: 100% āāāāāāāāāāāā 44/44 21.7it/s 2.0s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 17.4it/s 0.3s all 155 105 0.569 0.324 0.369 0.154 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 32/180 2.71G 1.464 1.744 1.763 12 640: 100% āāāāāāāāāāāā 44/44 21.3it/s 2.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 19.9it/s 0.3s all 155 105 0.554 0.592 0.525 0.242 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 33/180 2.71G 1.426 1.74 1.727 12 640: 100% āāāāāāāāāāāā 44/44 21.7it/s 2.0s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 20.2it/s 0.2s all 155 105 0.564 0.514 0.558 0.261 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 34/180 2.71G 1.446 1.751 1.744 19 640: 100% āāāāāāāāāāāā 44/44 21.0it/s 2.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 18.7it/s 0.3s all 155 105 0.601 0.524 0.557 0.246 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 35/180 2.71G 1.474 1.739 1.744 12 640: 100% āāāāāāāāāāāā 44/44 20.0it/s 2.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 17.1it/s 0.3s all 155 105 0.505 0.633 0.513 0.249 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 36/180 2.71G 1.459 1.74 1.735 28 640: 100% āāāāāāāāāāāā 44/44 20.9it/s 2.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 20.3it/s 0.2s all 155 105 0.45 0.495 0.424 0.215 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 37/180 2.71G 1.412 1.708 1.714 15 640: 100% āāāāāāāāāāāā 44/44 20.9it/s 2.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 20.0it/s 0.2s all 155 105 0.659 0.627 0.644 0.303 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 38/180 2.71G 1.331 1.607 1.67 21 640: 100% āāāāāāāāāāāā 44/44 19.4it/s 2.3s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 19.3it/s 0.3s all 155 105 0.477 0.638 0.504 0.269 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 39/180 2.71G 1.521 1.778 1.774 14 640: 100% āāāāāāāāāāāā 44/44 20.2it/s 2.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 19.2it/s 0.3s all 155 105 0.529 0.543 0.482 0.209 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 40/180 2.71G 1.305 1.676 1.646 14 640: 100% āāāāāāāāāāāā 44/44 21.1it/s 2.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 19.4it/s 0.3s all 155 105 0.697 0.638 0.683 0.336 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 41/180 2.71G 1.41 1.684 1.701 19 640: 100% āāāāāāāāāāāā 44/44 21.3it/s 2.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 18.9it/s 0.3s all 155 105 0.585 0.698 0.639 0.304 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 42/180 2.71G 1.37 1.535 1.678 21 640: 100% āāāāāāāāāāāā 44/44 21.1it/s 2.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 18.9it/s 0.3s all 155 105 0.612 0.533 0.526 0.292 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 43/180 2.71G 1.367 1.611 1.665 15 640: 100% āāāāāāāāāāāā 44/44 21.1it/s 2.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 17.4it/s 0.3s all 155 105 0.507 0.538 0.528 0.218 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 44/180 2.71G 1.331 1.566 1.658 23 640: 100% āāāāāāāāāāāā 44/44 21.0it/s 2.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 20.7it/s 0.2s all 155 105 0.534 0.61 0.596 0.287 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 45/180 2.71G 1.336 1.596 1.643 18 640: 100% āāāāāāāāāāāā 44/44 21.7it/s 2.0s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 17.4it/s 0.3s all 155 105 0.542 0.609 0.571 0.291 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 46/180 2.71G 1.365 1.586 1.666 22 640: 100% āāāāāāāāāāāā 44/44 22.0it/s 2.0s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 20.6it/s 0.2s all 155 105 0.72 0.59 0.599 0.287 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 47/180 2.71G 1.4 1.617 1.699 10 640: 100% āāāāāāāāāāāā 44/44 20.4it/s 2.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 20.4it/s 0.2s all 155 105 0.662 0.6 0.572 0.256 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 48/180 2.71G 1.361 1.598 1.659 18 640: 100% āāāāāāāāāāāā 44/44 20.3it/s 2.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 18.1it/s 0.3s all 155 105 0.628 0.743 0.654 0.321 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 49/180 2.71G 1.354 1.595 1.648 19 640: 100% āāāāāāāāāāāā 44/44 19.9it/s 2.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 18.5it/s 0.3s all 155 105 0.705 0.686 0.682 0.35 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 50/180 2.71G 1.253 1.541 1.604 16 640: 100% āāāāāāāāāāāā 44/44 21.2it/s 2.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 16.5it/s 0.3s all 155 105 0.565 0.648 0.571 0.272 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 51/180 2.71G 1.345 1.608 1.638 39 640: 100% āāāāāāāāāāāā 44/44 20.2it/s 2.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 19.4it/s 0.3s all 155 105 0.618 0.667 0.647 0.314 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 52/180 2.71G 1.304 1.498 1.603 27 640: 100% āāāāāāāāāāāā 44/44 19.9it/s 2.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 18.4it/s 0.3s all 155 105 0.657 0.581 0.596 0.315 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 53/180 2.71G 1.284 1.541 1.604 22 640: 100% āāāāāāāāāāāā 44/44 20.4it/s 2.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 20.4it/s 0.2s all 155 105 0.515 0.657 0.567 0.28 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 54/180 2.71G 1.306 1.536 1.621 37 640: 100% āāāāāāāāāāāā 44/44 21.3it/s 2.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 20.2it/s 0.2s all 155 105 0.601 0.533 0.526 0.27 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 55/180 2.71G 1.261 1.549 1.619 12 640: 100% āāāāāāāāāāāā 44/44 20.9it/s 2.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 19.9it/s 0.3s all 155 105 0.61 0.714 0.65 0.327 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 56/180 2.71G 1.279 1.499 1.614 17 640: 100% āāāāāāāāāāāā 44/44 20.0it/s 2.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 19.2it/s 0.3s all 155 105 0.625 0.581 0.583 0.319 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 57/180 2.71G 1.275 1.485 1.608 15 640: 100% āāāāāāāāāāāā 44/44 20.4it/s 2.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 18.3it/s 0.3s all 155 105 0.532 0.524 0.479 0.237 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 58/180 2.71G 1.262 1.506 1.583 18 640: 100% āāāāāāāāāāāā 44/44 20.8it/s 2.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 18.7it/s 0.3s all 155 105 0.541 0.667 0.619 0.305 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 59/180 2.71G 1.297 1.559 1.6 13 640: 100% āāāāāāāāāāāā 44/44 20.0it/s 2.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 20.6it/s 0.2s all 155 105 0.544 0.524 0.526 0.27 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 60/180 2.71G 1.288 1.507 1.642 18 640: 100% āāāāāāāāāāāā 44/44 19.7it/s 2.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 16.9it/s 0.3s all 155 105 0.55 0.667 0.591 0.305 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 61/180 2.71G 1.251 1.491 1.566 16 640: 100% āāāāāāāāāāāā 44/44 19.9it/s 2.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 19.8it/s 0.3s all 155 105 0.639 0.624 0.604 0.276 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 62/180 2.71G 1.325 1.502 1.643 10 640: 100% āāāāāāāāāāāā 44/44 20.3it/s 2.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 19.6it/s 0.3s all 155 105 0.664 0.724 0.706 0.364 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 63/180 2.71G 1.231 1.45 1.564 19 640: 100% āāāāāāāāāāāā 44/44 21.1it/s 2.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 15.4it/s 0.3s all 155 105 0.655 0.533 0.567 0.265 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 64/180 2.71G 1.251 1.46 1.589 15 640: 100% āāāāāāāāāāāā 44/44 20.5it/s 2.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 18.1it/s 0.3s all 155 105 0.631 0.648 0.617 0.311 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 65/180 2.71G 1.264 1.447 1.594 16 640: 100% āāāāāāāāāāāā 44/44 20.6it/s 2.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 17.3it/s 0.3s all 155 105 0.612 0.533 0.543 0.248 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 66/180 2.71G 1.216 1.423 1.581 17 640: 100% āāāāāāāāāāāā 44/44 20.3it/s 2.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 18.4it/s 0.3s all 155 105 0.734 0.724 0.73 0.413 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 67/180 2.71G 1.208 1.393 1.547 14 640: 100% āāāāāāāāāāāā 44/44 18.4it/s 2.4s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 18.5it/s 0.3s all 155 105 0.738 0.724 0.718 0.38 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 68/180 2.71G 1.268 1.455 1.594 19 640: 100% āāāāāāāāāāāā 44/44 20.3it/s 2.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 20.0it/s 0.3s all 155 105 0.667 0.733 0.665 0.366 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 69/180 2.71G 1.241 1.414 1.563 15 640: 100% āāāāāāāāāāāā 44/44 19.5it/s 2.3s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 19.4it/s 0.3s all 155 105 0.534 0.545 0.54 0.276 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 70/180 2.71G 1.322 1.489 1.595 13 640: 100% āāāāāāāāāāāā 44/44 19.5it/s 2.3s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 18.3it/s 0.3s all 155 105 0.794 0.588 0.673 0.362 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 71/180 2.71G 1.195 1.45 1.582 19 640: 100% āāāāāāāāāāāā 44/44 20.0it/s 2.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 18.0it/s 0.3s all 155 105 0.686 0.552 0.638 0.338 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 72/180 2.71G 1.228 1.481 1.612 21 640: 100% āāāāāāāāāāāā 44/44 20.3it/s 2.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 18.7it/s 0.3s all 155 105 0.658 0.679 0.679 0.395 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 73/180 2.71G 1.274 1.497 1.592 9 640: 100% āāāāāāāāāāāā 44/44 20.7it/s 2.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 20.0it/s 0.3s all 155 105 0.651 0.745 0.711 0.358 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 74/180 2.71G 1.219 1.453 1.561 21 640: 100% āāāāāāāāāāāā 44/44 19.8it/s 2.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 19.1it/s 0.3s all 155 105 0.692 0.714 0.728 0.406 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 75/180 2.71G 1.235 1.37 1.578 17 640: 100% āāāāāāāāāāāā 44/44 19.9it/s 2.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 19.1it/s 0.3s all 155 105 0.644 0.654 0.672 0.352 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 76/180 2.71G 1.25 1.5 1.561 18 640: 100% āāāāāāāāāāāā 44/44 20.9it/s 2.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 17.5it/s 0.3s all 155 105 0.531 0.583 0.615 0.293 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 77/180 2.71G 1.213 1.452 1.569 25 640: 100% āāāāāāāāāāāā 44/44 19.9it/s 2.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 19.5it/s 0.3s all 155 105 0.739 0.705 0.741 0.408 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 78/180 2.72G 1.273 1.409 1.59 23 640: 100% āāāāāāāāāāāā 44/44 20.0it/s 2.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 20.3it/s 0.2s all 155 105 0.695 0.686 0.675 0.346 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 79/180 2.72G 1.15 1.41 1.531 27 640: 100% āāāāāāāāāāāā 44/44 20.3it/s 2.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 18.9it/s 0.3s all 155 105 0.664 0.657 0.687 0.375 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 80/180 2.72G 1.158 1.347 1.534 15 640: 100% āāāāāāāāāāāā 44/44 20.5it/s 2.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 17.0it/s 0.3s all 155 105 0.786 0.695 0.735 0.405 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 81/180 2.72G 1.281 1.456 1.596 16 640: 100% āāāāāāāāāāāā 44/44 20.6it/s 2.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 19.6it/s 0.3s all 155 105 0.689 0.648 0.682 0.427 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 82/180 2.72G 1.16 1.384 1.539 18 640: 100% āāāāāāāāāāāā 44/44 19.6it/s 2.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 17.6it/s 0.3s all 155 105 0.729 0.718 0.743 0.424 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 83/180 2.72G 1.226 1.407 1.576 12 640: 100% āāāāāāāāāāāā 44/44 20.1it/s 2.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 20.1it/s 0.2s all 155 105 0.609 0.629 0.615 0.358 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 84/180 2.72G 1.168 1.351 1.543 19 640: 100% āāāāāāāāāāāā 44/44 19.6it/s 2.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 19.2it/s 0.3s all 155 105 0.636 0.681 0.681 0.404 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 85/180 2.72G 1.148 1.333 1.514 9 640: 100% āāāāāāāāāāāā 44/44 20.8it/s 2.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 18.2it/s 0.3s all 155 105 0.732 0.677 0.69 0.337 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 86/180 2.72G 1.194 1.311 1.54 14 640: 100% āāāāāāāāāāāā 44/44 20.3it/s 2.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 15.4it/s 0.3s all 155 105 0.66 0.703 0.637 0.329 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 87/180 2.72G 1.174 1.4 1.551 19 640: 100% āāāāāāāāāāāā 44/44 19.6it/s 2.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 18.3it/s 0.3s all 155 105 0.753 0.666 0.729 0.428 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 88/180 2.72G 1.148 1.376 1.513 21 640: 100% āāāāāāāāāāāā 44/44 20.5it/s 2.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 19.6it/s 0.3s all 155 105 0.647 0.594 0.618 0.362 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 89/180 2.72G 1.181 1.392 1.56 15 640: 100% āāāāāāāāāāāā 44/44 20.3it/s 2.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 19.7it/s 0.3s all 155 105 0.726 0.695 0.735 0.436 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 90/180 2.72G 1.151 1.366 1.527 20 640: 100% āāāāāāāāāāāā 44/44 20.1it/s 2.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 19.4it/s 0.3s all 155 105 0.673 0.762 0.719 0.395 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 91/180 2.72G 1.18 1.407 1.516 14 640: 100% āāāāāāāāāāāā 44/44 19.6it/s 2.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 19.3it/s 0.3s all 155 105 0.703 0.7 0.708 0.42 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 92/180 2.72G 1.186 1.341 1.55 20 640: 100% āāāāāāāāāāāā 44/44 19.2it/s 2.3s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 19.5it/s 0.3s all 155 105 0.74 0.732 0.777 0.456 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 93/180 2.72G 1.123 1.287 1.513 14 640: 100% āāāāāāāāāāāā 44/44 20.1it/s 2.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 20.2it/s 0.2s all 155 105 0.739 0.724 0.765 0.488 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 94/180 2.72G 1.156 1.295 1.513 11 640: 100% āāāāāāāāāāāā 44/44 19.3it/s 2.3s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 20.0it/s 0.2s all 155 105 0.7 0.71 0.729 0.434 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 95/180 2.72G 1.155 1.256 1.508 19 640: 100% āāāāāāāāāāāā 44/44 19.7it/s 2.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 20.2it/s 0.2s all 155 105 0.651 0.695 0.701 0.434 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 96/180 2.72G 1.154 1.273 1.494 19 640: 100% āāāāāāāāāāāā 44/44 20.5it/s 2.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 21.0it/s 0.2s all 155 105 0.689 0.648 0.696 0.419 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 97/180 2.72G 1.173 1.298 1.521 12 640: 100% āāāāāāāāāāāā 44/44 20.5it/s 2.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 19.9it/s 0.3s all 155 105 0.764 0.667 0.743 0.44 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 98/180 2.72G 1.148 1.326 1.508 11 640: 100% āāāāāāāāāāāā 44/44 19.9it/s 2.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 15.7it/s 0.3s all 155 105 0.74 0.829 0.795 0.483 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 99/180 2.72G 1.13 1.253 1.474 14 640: 100% āāāāāāāāāāāā 44/44 20.7it/s 2.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 16.3it/s 0.3s all 155 105 0.665 0.8 0.747 0.43 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 100/180 2.72G 1.137 1.265 1.539 15 640: 100% āāāāāāāāāāāā 44/44 21.3it/s 2.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 19.9it/s 0.3s all 155 105 0.662 0.629 0.662 0.38 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 101/180 2.72G 1.15 1.258 1.521 11 640: 100% āāāāāāāāāāāā 44/44 19.5it/s 2.3s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 19.5it/s 0.3s all 155 105 0.658 0.66 0.7 0.405 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 102/180 2.72G 1.14 1.295 1.497 19 640: 100% āāāāāāāāāāāā 44/44 20.6it/s 2.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 20.0it/s 0.3s all 155 105 0.71 0.695 0.674 0.365 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 103/180 2.72G 1.087 1.235 1.456 24 640: 100% āāāāāāāāāāāā 44/44 20.6it/s 2.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 19.0it/s 0.3s all 155 105 0.793 0.79 0.775 0.452 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 104/180 2.72G 1.058 1.266 1.464 16 640: 100% āāāāāāāāāāāā 44/44 20.3it/s 2.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 18.9it/s 0.3s all 155 105 0.662 0.762 0.739 0.462 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 105/180 2.72G 1.109 1.288 1.501 20 640: 100% āāāāāāāāāāāā 44/44 20.4it/s 2.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 15.5it/s 0.3s all 155 105 0.653 0.682 0.67 0.393 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 106/180 2.72G 1.103 1.271 1.462 23 640: 100% āāāāāāāāāāāā 44/44 20.7it/s 2.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 19.2it/s 0.3s all 155 105 0.769 0.761 0.805 0.47 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 107/180 2.72G 1.123 1.242 1.483 13 640: 100% āāāāāāāāāāāā 44/44 20.3it/s 2.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 17.6it/s 0.3s all 155 105 0.762 0.733 0.737 0.408 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 108/180 2.72G 1.141 1.231 1.5 25 640: 100% āāāāāāāāāāāā 44/44 19.9it/s 2.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 20.0it/s 0.3s all 155 105 0.724 0.695 0.759 0.429 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 109/180 2.72G 1.118 1.278 1.482 18 640: 100% āāāāāāāāāāāā 44/44 18.9it/s 2.3s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 20.0it/s 0.3s all 155 105 0.706 0.6 0.666 0.399 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 110/180 2.72G 1.054 1.226 1.459 17 640: 100% āāāāāāāāāāāā 44/44 19.7it/s 2.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 20.0it/s 0.3s all 155 105 0.731 0.726 0.766 0.466 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 111/180 2.72G 1.155 1.3 1.496 24 640: 100% āāāāāāāāāāāā 44/44 20.0it/s 2.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 19.6it/s 0.3s all 155 105 0.798 0.638 0.719 0.433 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 112/180 2.72G 1.103 1.218 1.469 21 640: 100% āāāāāāāāāāāā 44/44 20.4it/s 2.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 18.6it/s 0.3s all 155 105 0.685 0.724 0.748 0.434 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 113/180 2.72G 1.143 1.239 1.502 15 640: 100% āāāāāāāāāāāā 44/44 19.9it/s 2.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 19.8it/s 0.3s all 155 105 0.588 0.6 0.589 0.32 EarlyStopping: Training stopped early as no improvement observed in last 20 epochs. Best results observed at epoch 93, best model saved as best.pt. To update EarlyStopping(patience=20) pass a new patience value, i.e. `patience=300` or use `patience=0` to disable EarlyStopping. 113 epochs completed in 0.093 hours. Optimizer stripped from /content/drive/MyDrive/FHNW/HS_25/DLBS/minichallenge_hs25_object_detection/MC/runs/detect/runs/regularize/regularize_run_1/weights/last.pt, 6.3MB Optimizer stripped from /content/drive/MyDrive/FHNW/HS_25/DLBS/minichallenge_hs25_object_detection/MC/runs/detect/runs/regularize/regularize_run_1/weights/best.pt, 6.3MB Validating /content/drive/MyDrive/FHNW/HS_25/DLBS/minichallenge_hs25_object_detection/MC/runs/detect/runs/regularize/regularize_run_1/weights/best.pt... Ultralytics 8.3.252 š Python-3.12.12 torch-2.9.0+cu126 CUDA:0 (NVIDIA H100 80GB HBM3, 81224MiB) Model summary (fused): 72 layers, 3,005,843 parameters, 0 gradients, 8.1 GFLOPs Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 4.9it/s 1.0s all 155 105 0.791 0.657 0.764 0.509 Speed: 0.1ms preprocess, 2.8ms inference, 0.0ms loss, 0.6ms postprocess per image Results saved to /content/drive/MyDrive/FHNW/HS_25/DLBS/minichallenge_hs25_object_detection/MC/runs/detect/runs/regularize/regularize_run_1
InĀ [Ā ]:
# --- CONFIGURATION ---
DRIVE_MODEL_DIR = './yolov8_results/'
RUNS_DIR = 'runs/detect/runs/regularize/regularize_run_1'
BEST_WEIGHTS_FILENAME = 'best_regularize_1.pt'
OUTPUT_WEIGHTS_NAME = 'weights_regularize_1.pt'
os.makedirs(DRIVE_MODEL_DIR, exist_ok=True)
InĀ [Ā ]:
save_weights_to_drive(RUNS_DIR, DRIVE_MODEL_DIR, BEST_WEIGHTS_FILENAME, OUTPUT_WEIGHTS_NAME)
ā Best weights saved: best_regularize_1.pt ā Last weights saved: weights_regularize_1.pt
InĀ [Ā ]:
import pandas as pd
import matplotlib.pyplot as plt
# Run 1
visualize_training_results(RUNS_DIR)
============================================================ š DETAILED TRAINING STATISTICS ============================================================ Total Epochs: 113 šÆ Best mAP50: 0.8049 (Epoch 105) šÆ Best mAP50-95: 0.4882 (Epoch 92) š Final Metrics: mAP50: 0.5893 mAP50-95: 0.3196 Precision: 0.5885 Recall: 0.6000 ā ļø Possible overfitting detected: mAP50 dropped by 0.216 ============================================================
"Class Loss" measures how well the model identifies what an object is (Santa vs. Background). Initially, the model has no idea what a Santa looks like, so it makes huge errors. Class Loss keeps falling after DFL stabilizes around epoch 80, but mAP stays somewhat flat, which could mean that the model is starting to memorize pixel patterns rather than learning features. However, the model is still struggling a bit with localization (higher than optimal box loss) as well as with classification (higher than desired class loss).
The mAP curves show a strong upward trend, reaching a peak of 0.805 for mAP50, which is significantly higher and more stable than your overfitted run. While there are minor fluctuations, the overall growth indicates the model is successfully generalizing its knowledge to the validation set.
Precision and recall converge toward each other, meaning the model is becoming equally good at avoiding false positives and finding all Santas, but there is room for imrpovement.
Total loss curve demonstrates stable optimization process, no erratic spikes.
Early stopping here might not be the best decision, since the training is a bit unstable. We could use optimizer and also ensure smoother training with `momentum'=0.9 or try 'warmup_epochs', where we gradually increase learning rate in the intial epochs (our smaller dataset is less forgiving of a bad start). We can tune this parameters in 04 Notebook.
In Overfitting check, the generalization gap is minimized.
InĀ [Ā ]:
# Run diagnostics
df = diagnose_training_results('runs/detect/runs/regularize/regularize_run_1')
================================================================================ š TRAINING DIAGNOSTICS ================================================================================ š FINAL METRICS (Epoch 113): mAP50: 0.5893 mAP50-95: 0.3196 Precision: 0.5885 Recall: 0.6000 šÆ BEST METRICS (Epoch 106): mAP50: 0.8049 mAP50-95: 0.4696 Precision: 0.7691 Recall: 0.7613 š LOSS VALUES: Final Box Loss: 1.1428 Final Class Loss: 1.2392 Final DFL Loss: 1.5019 ā ļø POTENTIAL ISSUES: ā ļø High box loss - Struggling with localization ā ļø High class loss - Struggling with classification ā ļø Overfitting detected - mAP dropped 0.216 ================================================================================
Checking the model performance on an unseen test setĀ¶
InĀ [Ā ]:
# On a TEST SET
# Run validation on the test split
model = YOLO('./yolov8_results/best_regularize_1.pt')
metrics = model.val(data=dataset_yaml, split='test')
print(f"Test results saved to: {metrics.save_dir}")
# Precision, recall, and mAP results
precision = metrics.results_dict['metrics/precision(B)']
recall = metrics.results_dict['metrics/recall(B)']
mAP50 = metrics.results_dict['metrics/mAP50(B)']
mAP50_95 = metrics.results_dict['metrics/mAP50-95(B)']
print(f"Precision: {precision:.2f}")
print(f"Recall: {recall:.2f}")
print(f"mAP50: {mAP50:.2f}")
print(f"mAP50-95: {mAP50_95:.2f}")
Ultralytics 8.3.252 š Python-3.12.12 torch-2.9.0+cu126 CUDA:0 (NVIDIA A100-SXM4-40GB, 40507MiB) Model summary (fused): 72 layers, 11,125,971 parameters, 0 gradients, 28.4 GFLOPs val: Fast image access ā (ping: 0.6Ā±0.2 ms, read: 23.0Ā±5.9 MB/s, size: 28.3 KB) val: Scanning /content/drive/MyDrive/FHNW/HS_25/DLBS/minichallenge_hs25_object_detection/MC/Santa-10/test/labels.cache... 95 images, 42 backgrounds, 0 corrupt: 100% āāāāāāāāāāāā 95/95 36.2Mit/s 0.0s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 6/6 3.6it/s 1.7s all 95 53 0.727 0.803 0.838 0.507 Speed: 2.3ms preprocess, 1.7ms inference, 0.0ms loss, 1.9ms postprocess per image Results saved to /content/drive/MyDrive/FHNW/HS_25/DLBS/minichallenge_hs25_object_detection/MC/runs/detect/val17 Test results saved to: /content/drive/MyDrive/FHNW/HS_25/DLBS/minichallenge_hs25_object_detection/MC/runs/detect/val17 Precision: 0.73 Recall: 0.80 mAP50: 0.84 mAP50-95: 0.51
InĀ [Ā ]:
%cd '/content/drive/MyDrive/FHNW/HS_25/DLBS/minichallenge_hs25_object_detection/MC'
/content/drive/MyDrive/FHNW/HS_25/DLBS/minichallenge_hs25_object_detection/MC
InĀ [Ā ]:
from PIL import Image as PILImage
import matplotlib.pyplot as plt
# F1 curve
f1_path = os.path.join('runs/detect/val17', 'BoxF1_curve.png')
display(Image(filename=f1_path, width=600))
# Precision Recall curve
pr_path = os.path.join('runs/detect/val17', 'BoxPR_curve.png')
display(Image(filename=pr_path, width=600))
# Confusion matrix
cm_path = os.path.join('runs/detect/val17', 'confusion_matrix.png')
display(Image(filename=cm_path, width=600))
F1 score penalizes you if you miss Santas (low Recall) OR if you see them where they don't exist (low Precision). For a small dataset, peak at 0.805 is a good result, but it is true we deal with only one class. Optimal threshold seems to be around 0.5. If we go lower, we catch more Santas, but also more false alarms.
Precision-Recall (PR) Curve indicates model robustness. A model that reaches closer to the top-right corner is superior, because it is precise about the Santas it identifies.
Since we want to optimize fo less false negatives, we need to be OK with lower precision (after all we optimize for higher recall). This we can control with lowering the confidence threshold.
InĀ [Ā ]:
def show_image_results(path, batch=0):
"""Side-by-side view of Ground Truth vs Predictions."""
f_lab = os.path.join(path, f'val_batch{batch}_labels.jpg')
f_pre = os.path.join(path, f'val_batch{batch}_pred.jpg')
if not os.path.exists(f_lab):
return print(f"Missing files in {path}")
fig, ax = plt.subplots(1, 2, figsize=(18, 9))
ax[0].imshow(PILImage.open(f_lab))
ax[0].set_title("Labels (Actual)")
ax[1].imshow(PILImage.open(f_pre))
ax[1].set_title("Predictions (Model)")
for a in ax: a.axis('off')
plt.tight_layout()
plt.show()
show_image_results('runs/detect/val17', batch=0)
show_image_results('runs/detect/val17', batch=2)
The visual inspection reveals that some false negatives/positives might be the consequence of the noise in annotation. Firstly, the definition of what is a Santa would need to be revisited and addtional instances shoud be annotated. Secondly, some bounding boxes (not visible here) would need to be more narrow, containing less superfluous pixels.
Run 2: Small YOLOĀ¶
InĀ [Ā ]:
# Load overfitted weights instead of pretrained
# Best model from before, YOLO s
model = YOLO('models_cache/yolov8s.pt')
# Use even lighter regularization, finetune_from_overfit
regularized_config_2 = {
'data': dataset_yaml,
'epochs': 150,
'batch': 16,
'patience': 25,
'device': 0,
'augment': True,
'degrees': 5.0,
'translate': 0.1,
'scale': 0.3,
'fliplr': 0.5,
'mosaic': 0.3, # Very light mosaic
'mixup': 0.0,
'lr0': 0.001, # a bit stronger L2
'lrf': 0.1,
'momentum': 0.937,
'weight_decay': 0.008,
'dropout': 0.2,
'optimizer': 'Adam',
'verbose': True,
'save': True,
'project': 'runs/regularize',
'name': 'regularize_run_2',
'val': True,
'exist_ok': True
}
InĀ [Ā ]:
# # --- RUN 2
# --- TRAIN ---
print("š Starting training (designed NOT to overfit) ...")
results = model.train(**regularized_config_2)
š Starting training (designed NOT to overfit) ... Ultralytics 8.3.252 š Python-3.12.12 torch-2.9.0+cu126 CUDA:0 (NVIDIA A100-SXM4-40GB, 40507MiB) engine/trainer: agnostic_nms=False, amp=True, augment=True, auto_augment=randaugment, batch=16, bgr=0.0, box=7.5, cache=False, cfg=None, classes=None, close_mosaic=10, cls=0.5, compile=False, conf=None, copy_paste=0.0, copy_paste_mode=flip, cos_lr=True, cutmix=0.0, data=Santa-10/data.yaml, degrees=5.0, deterministic=True, device=0, dfl=1.5, dnn=False, dropout=0.0, dynamic=False, embed=None, epochs=150, erasing=0.4, exist_ok=True, fliplr=0.5, flipud=0.0, format=torchscript, fraction=1.0, freeze=None, half=False, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, imgsz=512, int8=False, iou=0.7, keras=False, kobj=1.0, line_width=None, lr0=0.001, lrf=0.1, mask_ratio=4, max_det=300, mixup=0.0, mode=train, model=yolov8_results/best_overfit_4.pt, momentum=0.937, mosaic=0.3, multi_scale=False, name=regularize_run_2, nbs=64, nms=False, opset=None, optimize=False, optimizer=SGD, overlap_mask=True, patience=25, perspective=0.0, plots=True, pose=12.0, pretrained=True, profile=False, project=runs/regularize, rect=False, resume=False, retina_masks=False, save=True, save_conf=False, save_crop=False, save_dir=/content/drive/MyDrive/FHNW/HS_25/DLBS/minichallenge_hs25_object_detection/MC/runs/detect/runs/regularize/regularize_run_2, save_frames=False, save_json=False, save_period=-1, save_txt=False, scale=0.3, seed=0, shear=0.0, show=False, show_boxes=True, show_conf=True, show_labels=True, simplify=True, single_cls=False, source=None, split=val, stream_buffer=False, task=detect, time=None, tracker=botsort.yaml, translate=0.1, val=True, verbose=True, vid_stride=1, visualize=False, warmup_bias_lr=0.1, warmup_epochs=3.0, warmup_momentum=0.8, weight_decay=5e-05, workers=8, workspace=None from n params module arguments 0 -1 1 928 ultralytics.nn.modules.conv.Conv [3, 32, 3, 2] 1 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2] 2 -1 1 29056 ultralytics.nn.modules.block.C2f [64, 64, 1, True] 3 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2] 4 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True] 5 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2] 6 -1 2 788480 ultralytics.nn.modules.block.C2f [256, 256, 2, True] 7 -1 1 1180672 ultralytics.nn.modules.conv.Conv [256, 512, 3, 2] 8 -1 1 1838080 ultralytics.nn.modules.block.C2f [512, 512, 1, True] 9 -1 1 656896 ultralytics.nn.modules.block.SPPF [512, 512, 5] 10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] 12 -1 1 591360 ultralytics.nn.modules.block.C2f [768, 256, 1] 13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] 15 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1] 16 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] 17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1] 18 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1] 19 -1 1 590336 ultralytics.nn.modules.conv.Conv [256, 256, 3, 2] 20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1] 21 -1 1 1969152 ultralytics.nn.modules.block.C2f [768, 512, 1] 22 [15, 18, 21] 1 2116435 ultralytics.nn.modules.head.Detect [1, [128, 256, 512]] Model summary: 129 layers, 11,135,987 parameters, 11,135,971 gradients, 28.6 GFLOPs Transferred 355/355 items from pretrained weights Freezing layer 'model.22.dfl.conv.weight' AMP: running Automatic Mixed Precision (AMP) checks... AMP: checks passed ā train: Fast image access ā (ping: 0.5Ā±0.2 ms, read: 21.0Ā±13.7 MB/s, size: 27.0 KB) train: Scanning /content/drive/MyDrive/FHNW/HS_25/DLBS/minichallenge_hs25_object_detection/MC/Santa-10/train/labels... 700 images, 327 backgrounds, 0 corrupt: 100% āāāāāāāāāāāā 700/700 148.2it/s 4.7s train: New cache created: /content/drive/MyDrive/FHNW/HS_25/DLBS/minichallenge_hs25_object_detection/MC/Santa-10/train/labels.cache albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8)) val: Fast image access ā (ping: 1.5Ā±1.1 ms, read: 5.2Ā±6.1 MB/s, size: 28.7 KB) val: Scanning /content/drive/MyDrive/FHNW/HS_25/DLBS/minichallenge_hs25_object_detection/MC/Santa-10/valid/labels... 155 images, 54 backgrounds, 0 corrupt: 100% āāāāāāāāāāāā 155/155 157.3it/s 1.0s val: New cache created: /content/drive/MyDrive/FHNW/HS_25/DLBS/minichallenge_hs25_object_detection/MC/Santa-10/valid/labels.cache Plotting labels to /content/drive/MyDrive/FHNW/HS_25/DLBS/minichallenge_hs25_object_detection/MC/runs/detect/runs/regularize/regularize_run_2/labels.jpg... optimizer: SGD(lr=0.001, momentum=0.937) with parameter groups 57 weight(decay=0.0), 64 weight(decay=5e-05), 63 bias(decay=0.0) Image sizes 512 train, 512 val Using 8 dataloader workers Logging results to /content/drive/MyDrive/FHNW/HS_25/DLBS/minichallenge_hs25_object_detection/MC/runs/detect/runs/regularize/regularize_run_2 Starting training for 150 epochs... Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 1/150 3.12G 1.435 2.845 1.835 33 512: 100% āāāāāāāāāāāā 44/44 8.5it/s 5.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 8.2it/s 0.6s all 155 105 0.676 0.619 0.643 0.421 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 2/150 3.15G 1.206 1.993 1.619 6 512: 100% āāāāāāāāāāāā 44/44 11.3it/s 3.9s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 9.9it/s 0.5s all 155 105 0.564 0.705 0.655 0.44 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 3/150 3.17G 1.099 1.594 1.537 6 512: 100% āāāāāāāāāāāā 44/44 11.5it/s 3.8s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 9.6it/s 0.5s all 155 105 0.657 0.724 0.723 0.478 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 4/150 3.17G 0.9899 1.348 1.44 10 512: 100% āāāāāāāāāāāā 44/44 11.1it/s 3.9s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 8.9it/s 0.6s all 155 105 0.737 0.719 0.771 0.51 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 5/150 3.2G 0.9165 1.108 1.388 12 512: 100% āāāāāāāāāāāā 44/44 12.0it/s 3.7s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 10.3it/s 0.5s all 155 105 0.764 0.743 0.807 0.558 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 6/150 3.2G 0.8614 1.01 1.339 10 512: 100% āāāāāāāāāāāā 44/44 11.8it/s 3.7s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 9.6it/s 0.5s all 155 105 0.725 0.78 0.791 0.562 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 7/150 3.2G 0.8596 0.985 1.336 9 512: 100% āāāāāāāāāāāā 44/44 11.5it/s 3.8s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 10.7it/s 0.5s all 155 105 0.789 0.821 0.836 0.607 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 8/150 3.2G 0.8281 0.9452 1.28 7 512: 100% āāāāāāāāāāāā 44/44 11.9it/s 3.7s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 10.4it/s 0.5s all 155 105 0.87 0.761 0.857 0.636 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 9/150 3.2G 0.7746 0.8104 1.249 9 512: 100% āāāāāāāāāāāā 44/44 11.5it/s 3.8s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 10.4it/s 0.5s all 155 105 0.774 0.849 0.861 0.624 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 10/150 3.2G 0.7553 0.8286 1.248 13 512: 100% āāāāāāāāāāāā 44/44 10.9it/s 4.0s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 10.4it/s 0.5s all 155 105 0.781 0.819 0.82 0.592 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 11/150 3.2G 0.744 0.7578 1.231 8 512: 100% āāāāāāāāāāāā 44/44 11.5it/s 3.8s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 10.3it/s 0.5s all 155 105 0.866 0.802 0.852 0.631 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 12/150 3.2G 0.7919 0.8855 1.241 8 512: 100% āāāāāāāāāāāā 44/44 11.3it/s 3.9s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 10.2it/s 0.5s all 155 105 0.83 0.81 0.859 0.642 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 13/150 3.2G 0.7249 0.7761 1.233 3 512: 100% āāāāāāāāāāāā 44/44 12.3it/s 3.6s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 10.1it/s 0.5s all 155 105 0.795 0.848 0.867 0.643 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 14/150 3.2G 0.7508 0.7291 1.22 5 512: 100% āāāāāāāāāāāā 44/44 11.9it/s 3.7s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 9.9it/s 0.5s all 155 105 0.812 0.821 0.847 0.624 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 15/150 3.2G 0.7172 0.7435 1.193 10 512: 100% āāāāāāāāāāāā 44/44 11.8it/s 3.7s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 8.2it/s 0.6s all 155 105 0.833 0.856 0.857 0.621 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 16/150 3.2G 0.7532 0.6875 1.222 6 512: 100% āāāāāāāāāāāā 44/44 11.8it/s 3.7s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 10.4it/s 0.5s all 155 105 0.844 0.81 0.832 0.621 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 17/150 3.2G 0.6876 0.6596 1.16 8 512: 100% āāāāāāāāāāāā 44/44 11.5it/s 3.8s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 10.2it/s 0.5s all 155 105 0.828 0.829 0.866 0.649 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 18/150 3.2G 0.7367 0.671 1.22 13 512: 100% āāāāāāāāāāāā 44/44 11.4it/s 3.9s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 10.2it/s 0.5s all 155 105 0.873 0.724 0.858 0.621 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 19/150 3.2G 0.7059 0.6419 1.21 8 512: 100% āāāāāāāāāāāā 44/44 12.1it/s 3.6s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 10.9it/s 0.5s all 155 105 0.823 0.838 0.879 0.636 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 20/150 3.2G 0.7019 0.6062 1.181 8 512: 100% āāāāāāāāāāāā 44/44 11.6it/s 3.8s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 9.4it/s 0.5s all 155 105 0.87 0.771 0.855 0.639 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 21/150 3.2G 0.6386 0.565 1.136 8 512: 100% āāāāāāāāāāāā 44/44 11.8it/s 3.7s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 10.4it/s 0.5s all 155 105 0.846 0.819 0.87 0.654 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 22/150 3.2G 0.6687 0.5771 1.145 7 512: 100% āāāāāāāāāāāā 44/44 12.1it/s 3.6s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 10.3it/s 0.5s all 155 105 0.86 0.816 0.873 0.647 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 23/150 3.2G 0.6901 0.6227 1.178 13 512: 100% āāāāāāāāāāāā 44/44 11.4it/s 3.8s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 10.6it/s 0.5s all 155 105 0.853 0.828 0.848 0.595 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 24/150 3.2G 0.6696 0.5857 1.15 7 512: 100% āāāāāāāāāāāā 44/44 11.7it/s 3.8s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 10.4it/s 0.5s all 155 105 0.777 0.81 0.817 0.596 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 25/150 3.2G 0.6884 0.5846 1.164 7 512: 100% āāāāāāāāāāāā 44/44 11.9it/s 3.7s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 9.6it/s 0.5s all 155 105 0.825 0.781 0.824 0.599 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 26/150 3.2G 0.6674 0.5969 1.147 6 512: 100% āāāāāāāāāāāā 44/44 11.5it/s 3.8s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 10.5it/s 0.5s all 155 105 0.796 0.782 0.837 0.62 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 27/150 3.2G 0.6107 0.5306 1.125 9 512: 100% āāāāāāāāāāāā 44/44 11.7it/s 3.8s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 10.0it/s 0.5s all 155 105 0.827 0.857 0.871 0.644 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 28/150 3.2G 0.6726 0.5525 1.152 10 512: 100% āāāāāāāāāāāā 44/44 11.4it/s 3.8s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 9.2it/s 0.5s all 155 105 0.804 0.862 0.846 0.617 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 29/150 3.2G 0.6767 0.5284 1.163 11 512: 100% āāāāāāāāāāāā 44/44 11.8it/s 3.7s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 11.0it/s 0.5s all 155 105 0.83 0.857 0.847 0.622 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 30/150 3.2G 0.6811 0.5462 1.15 9 512: 100% āāāāāāāāāāāā 44/44 12.3it/s 3.6s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 10.2it/s 0.5s all 155 105 0.85 0.819 0.854 0.66 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 31/150 3.2G 0.6403 0.5241 1.143 7 512: 100% āāāāāāāāāāāā 44/44 11.3it/s 3.9s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 10.9it/s 0.5s all 155 105 0.798 0.867 0.855 0.663 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 32/150 3.2G 0.601 0.529 1.086 7 512: 100% āāāāāāāāāāāā 44/44 12.0it/s 3.7s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 11.3it/s 0.4s all 155 105 0.823 0.797 0.851 0.645 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 33/150 3.2G 0.6501 0.5437 1.125 17 512: 100% āāāāāāāāāāāā 44/44 12.1it/s 3.6s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 8.6it/s 0.6s all 155 105 0.839 0.792 0.879 0.65 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 34/150 3.2G 0.6385 0.5051 1.12 12 512: 100% āāāāāāāāāāāā 44/44 11.3it/s 3.9s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 10.4it/s 0.5s all 155 105 0.854 0.829 0.859 0.634 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 35/150 3.2G 0.6639 0.5337 1.147 8 512: 100% āāāāāāāāāāāā 44/44 11.9it/s 3.7s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 10.7it/s 0.5s all 155 105 0.84 0.848 0.881 0.669 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 36/150 3.2G 0.671 0.5376 1.141 11 512: 100% āāāāāāāāāāāā 44/44 11.7it/s 3.7s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 9.9it/s 0.5s all 155 105 0.864 0.848 0.89 0.674 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 37/150 3.2G 0.5948 0.4657 1.087 10 512: 100% āāāāāāāāāāāā 44/44 11.5it/s 3.8s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 10.6it/s 0.5s all 155 105 0.844 0.874 0.892 0.686 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 38/150 3.2G 0.5827 0.482 1.091 8 512: 100% āāāāāāāāāāāā 44/44 11.9it/s 3.7s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 11.0it/s 0.5s all 155 105 0.795 0.887 0.866 0.667 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 39/150 3.2G 0.6105 0.4953 1.099 13 512: 100% āāāāāāāāāāāā 44/44 11.8it/s 3.7s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 9.5it/s 0.5s all 155 105 0.812 0.895 0.865 0.661 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 40/150 3.21G 0.59 0.4597 1.105 10 512: 100% āāāāāāāāāāāā 44/44 11.7it/s 3.8s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 10.8it/s 0.5s all 155 105 0.853 0.848 0.872 0.658 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 41/150 3.21G 0.6245 0.5171 1.105 7 512: 100% āāāāāāāāāāāā 44/44 11.6it/s 3.8s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 10.6it/s 0.5s all 155 105 0.898 0.848 0.876 0.645 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 42/150 3.21G 0.5823 0.4774 1.095 9 512: 100% āāāāāāāāāāāā 44/44 11.4it/s 3.8s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 10.4it/s 0.5s all 155 105 0.861 0.829 0.838 0.627 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 43/150 3.21G 0.5956 0.4669 1.102 13 512: 100% āāāāāāāāāāāā 44/44 12.2it/s 3.6s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 10.5it/s 0.5s all 155 105 0.873 0.819 0.819 0.608 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 44/150 3.21G 0.6022 0.4797 1.108 10 512: 100% āāāāāāāāāāāā 44/44 11.8it/s 3.7s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 10.1it/s 0.5s all 155 105 0.817 0.895 0.859 0.636 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 45/150 3.21G 0.5916 0.5103 1.109 10 512: 100% āāāāāāāāāāāā 44/44 11.3it/s 3.9s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 10.4it/s 0.5s all 155 105 0.847 0.848 0.872 0.65 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 46/150 3.21G 0.5883 0.4462 1.072 8 512: 100% āāāāāāāāāāāā 44/44 12.0it/s 3.7s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 10.2it/s 0.5s all 155 105 0.834 0.857 0.873 0.651 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 47/150 3.21G 0.6025 0.4831 1.091 9 512: 100% āāāāāāāāāāāā 44/44 11.8it/s 3.7s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 10.2it/s 0.5s all 155 105 0.834 0.857 0.859 0.647 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 48/150 3.21G 0.6188 0.4696 1.106 16 512: 100% āāāāāāāāāāāā 44/44 11.7it/s 3.8s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 10.4it/s 0.5s all 155 105 0.788 0.867 0.856 0.643 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 49/150 3.21G 0.5901 0.4575 1.117 10 512: 100% āāāāāāāāāāāā 44/44 11.7it/s 3.8s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 11.1it/s 0.5s all 155 105 0.874 0.781 0.811 0.609 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 50/150 3.21G 0.5522 0.4419 1.064 12 512: 100% āāāāāāāāāāāā 44/44 11.6it/s 3.8s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 10.8it/s 0.5s all 155 105 0.832 0.848 0.87 0.667 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 51/150 3.21G 0.5824 0.447 1.096 13 512: 100% āāāāāāāāāāāā 44/44 11.7it/s 3.8s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 9.7it/s 0.5s all 155 105 0.863 0.838 0.848 0.646 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 52/150 3.21G 0.544 0.4273 1.054 12 512: 100% āāāāāāāāāāāā 44/44 12.0it/s 3.7s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 10.6it/s 0.5s all 155 105 0.867 0.876 0.87 0.664 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 53/150 3.21G 0.5696 0.4542 1.08 13 512: 100% āāāāāāāāāāāā 44/44 11.5it/s 3.8s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 9.6it/s 0.5s all 155 105 0.894 0.806 0.867 0.661 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 54/150 3.21G 0.5522 0.4511 1.09 12 512: 100% āāāāāāāāāāāā 44/44 11.4it/s 3.9s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 10.6it/s 0.5s all 155 105 0.878 0.848 0.875 0.674 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 55/150 3.21G 0.5615 0.4552 1.084 6 512: 100% āāāāāāāāāāāā 44/44 12.0it/s 3.7s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 10.8it/s 0.5s all 155 105 0.84 0.914 0.882 0.672 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 56/150 3.21G 0.5716 0.4528 1.062 6 512: 100% āāāāāāāāāāāā 44/44 11.4it/s 3.9s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 9.5it/s 0.5s all 155 105 0.839 0.924 0.899 0.683 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 57/150 3.21G 0.5348 0.4348 1.058 13 512: 100% āāāāāāāāāāāā 44/44 11.8it/s 3.7s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 10.9it/s 0.5s all 155 105 0.901 0.876 0.896 0.663 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 58/150 3.21G 0.5438 0.4574 1.068 15 512: 100% āāāāāāāāāāāā 44/44 11.8it/s 3.7s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 10.4it/s 0.5s all 155 105 0.859 0.848 0.891 0.674 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 59/150 3.21G 0.5588 0.4326 1.07 7 512: 100% āāāāāāāāāāāā 44/44 11.6it/s 3.8s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 10.7it/s 0.5s all 155 105 0.832 0.914 0.881 0.681 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 60/150 3.21G 0.5704 0.447 1.087 12 512: 100% āāāāāāāāāāāā 44/44 11.9it/s 3.7s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 10.5it/s 0.5s all 155 105 0.831 0.89 0.877 0.667 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 61/150 3.21G 0.5869 0.4432 1.088 11 512: 100% āāāāāāāāāāāā 44/44 11.7it/s 3.8s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 8.8it/s 0.6s all 155 105 0.855 0.896 0.896 0.675 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 62/150 3.21G 0.5354 0.406 1.067 10 512: 100% āāāāāāāāāāāā 44/44 11.5it/s 3.8s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 10.5it/s 0.5s all 155 105 0.858 0.862 0.878 0.649 EarlyStopping: Training stopped early as no improvement observed in last 25 epochs. Best results observed at epoch 37, best model saved as best.pt. To update EarlyStopping(patience=25) pass a new patience value, i.e. `patience=300` or use `patience=0` to disable EarlyStopping. 62 epochs completed in 0.081 hours. Optimizer stripped from /content/drive/MyDrive/FHNW/HS_25/DLBS/minichallenge_hs25_object_detection/MC/runs/detect/runs/regularize/regularize_run_2/weights/last.pt, 22.5MB Optimizer stripped from /content/drive/MyDrive/FHNW/HS_25/DLBS/minichallenge_hs25_object_detection/MC/runs/detect/runs/regularize/regularize_run_2/weights/best.pt, 22.5MB Validating /content/drive/MyDrive/FHNW/HS_25/DLBS/minichallenge_hs25_object_detection/MC/runs/detect/runs/regularize/regularize_run_2/weights/best.pt... Ultralytics 8.3.252 š Python-3.12.12 torch-2.9.0+cu126 CUDA:0 (NVIDIA A100-SXM4-40GB, 40507MiB) Model summary (fused): 72 layers, 11,125,971 parameters, 0 gradients, 28.4 GFLOPs Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 5/5 3.5it/s 1.4s all 155 105 0.858 0.865 0.886 0.672 Speed: 0.1ms preprocess, 4.2ms inference, 0.0ms loss, 0.9ms postprocess per image Results saved to /content/drive/MyDrive/FHNW/HS_25/DLBS/minichallenge_hs25_object_detection/MC/runs/detect/runs/regularize/regularize_run_2
InĀ [Ā ]:
# --- CONFIGURATION ---
DRIVE_MODEL_DIR = DRIVE_MODEL_DIR
RUNS_DIR = 'runs/detect/runs/regularize/regularize_run_2' # Ultralytics default save path
BEST_WEIGHTS_FILENAME = 'best_regularize_2.pt'
OUTPUT_WEIGHTS_NAME = 'weights_regularize_2.pt'
# --- SAVE WEIGHTS TO GOOGLE DRIVE ---
save_weights_to_drive(RUNS_DIR, DRIVE_MODEL_DIR, BEST_WEIGHTS_FILENAME, OUTPUT_WEIGHTS_NAME)
InĀ [Ā ]:
# Run 2
visualize_training_results(RUNS_DIR)
============================================================ š DETAILED TRAINING STATISTICS ============================================================ Total Epochs: 62 šÆ Best mAP50: 0.8989 (Epoch 55) šÆ Best mAP50-95: 0.6863 (Epoch 36) š Final Metrics: mAP50: 0.8782 mAP50-95: 0.6494 Precision: 0.8579 Recall: 0.8624 ============================================================
All losses decrease smoothly and plateau early around epoch 30. Box and Class losses converge to ~0.5-0.6, showing good learning. Very stable training with no spikes after epoch 10.
mAP50 peaks around epoch 56 with ~0.9, then stabilizes at 87-89% with slight oscillations. Model learned quickly and plateaued early - small improvement after epoch 20. The model is too powerful for the task at hand.
Precision & recall show excellent balance hovering around 82-88% throughout training. High recall (92.4%) means very few missed Santas, which is what we want.
Overfitting check shows a generalizxation gap of ~1.0, indicating mild overfitting that doesn't worsen over time. Both losses converge after epoch 20, when model has found optimal balance.
The issue is almost no improvement after epoch 20-30, and the mode lwith 11M on such small dataset is an overkill. THe regularization might also be too intense.
Recommendations: Box loss should be 0.1-0.4, CLS loss should be similarly 0.1-0.4 and DLF loss ~0.5-1.0.
High Box Loss means either inaccurate coordinates or poor scale handling. We can in Tuning Notebook increase box gain in hyperparameters (box=10.0).
Look at the Target Ratio = Train Loss/Val Lossā = 1.0 or 1.5.
Test SetĀ¶
InĀ [Ā ]:
# On a TEST SET
# Run validation on the test split
model = YOLO('./yolov8_results/best_regularize_2.pt')
metrics = model.val(data=dataset_yaml, split='test')
print(f"Test results saved to: {metrics.save_dir}")
# Precision, recall, and mAP results
precision = metrics.results_dict['metrics/precision(B)']
recall = metrics.results_dict['metrics/recall(B)']
mAP50 = metrics.results_dict['metrics/mAP50(B)']
mAP50_95 = metrics.results_dict['metrics/mAP50-95(B)']
print(f"Precision: {precision:.2f}")
print(f"Recall: {recall:.2f}")
print(f"mAP50: {mAP50:.2f}")
print(f"mAP50-95: {mAP50_95:.2f}")
Ultralytics 8.3.252 š Python-3.12.12 torch-2.9.0+cpu CPU (AMD EPYC 7B12) Model summary (fused): 72 layers, 11,125,971 parameters, 0 gradients, 28.4 GFLOPs Downloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf': 100% āāāāāāāāāāāā 755.1KB 5.0MB/s 0.1s val: Fast image access ā (ping: 0.7Ā±0.2 ms, read: 0.0Ā±0.0 MB/s, size: 35.9 KB) val: Scanning /content/drive/MyDrive/FHNW/HS_25/DLBS/minichallenge_hs25_object_detection/MC/Santa-10/test/labels.cache... 95 images, 42 backgrounds, 0 corrupt: 100% āāāāāāāāāāāā 95/95 16.6Mit/s 0.0s Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 6/6 21.5s/it 2:09 all 95 53 0.875 0.923 0.927 0.732 Speed: 0.6ms preprocess, 110.5ms inference, 0.0ms loss, 0.6ms postprocess per image Results saved to /content/drive/MyDrive/FHNW/HS_25/DLBS/minichallenge_hs25_object_detection/MC/runs/detect/val4 Test results saved to: /content/drive/MyDrive/FHNW/HS_25/DLBS/minichallenge_hs25_object_detection/MC/runs/detect/val4 Precision: 0.87 Recall: 0.92 mAP50: 0.93 mAP50-95: 0.73
InĀ [Ā ]:
RUNS_DIR = 'runs/detect/val4'
# F1 curve
f1_path = os.path.join(RUNS_DIR, 'BoxF1_curve.png')
display(Image(filename=f1_path, width=600))
# Precision Recall curve
pr_path = os.path.join(RUNS_DIR, 'BoxPR_curve.png')
display(Image(filename=pr_path, width=600))
# Confusion matrix
cm_path = os.path.join(RUNS_DIR, 'confusion_matrix.png')
display(Image(filename=cm_path, width=600))
The clear F1 peak near 0.80 provides the optimal confidence threshold to use if we want the most balanced performance in a real-world application.
The PR curve demonstrates high robustness, meaning the model maintains high accuracy even as we lower thresholds to catch more "difficult" Santas.
The model correctly identified 51/53 Santas, only 2 Santas were missed (predicted as background), which is an excellent result for our goal of catching as many as possible. The model "hallucinated" 6 Santas where there was only background, which explains why our precision is slightly lower than your recall.
InĀ [Ā ]:
show_image_results(RUNS_DIR, 0)
print()
show_image_results(RUNS_DIR, 1)
Output hidden; open in https://colab.research.google.com to view.
Run 3: More regularized configuration on NANO YOLOĀ¶
Is nano version enouigh for our needs without sacrificing Recall?
InĀ [Ā ]:
# Run 3 - use config_2 on nano model
MODEL_PATH = './models_cache/yolov8n.pt'
model = YOLO(MODEL_PATH)
# Use even lighter regularization, finetune_from_overfit
regularized_config_3 = {
'data': dataset_yaml,
'epochs': 180,
'imgsz': 512,
'batch': 16,
'patience': 20,
'device': 'cpu',
'augment': True,
# Very light augmentation
'degrees': 2.0, # Rotation needs to be more subtle
'translate': 0.1,
'scale': 0.6, # 0.3 was very low and might provide enough variance.
'fliplr': 0.5,
'mosaic': 0.3, # improves generalization
'mixup': 0.0,
'lr0': 0.01,
'lrf': 0.01,
'momentum': 0.937,
'weight_decay': 0.00025, # reduce regularization from standard value
'dropout': 0.0,
'optimizer': 'AdamW',
'cos_lr': True,
'verbose': True,
'save': True,
'project': 'runs/regularize',
'name': 'regularize_run_3',
'val': True,
'exist_ok': True
}
InĀ [Ā ]:
# --- RUN 3
# --- TRAIN ---
print("š Starting training (regularized) ...")
results = model.train(**regularized_config_3)
InĀ [Ā ]:
# --- CONFIGURATION ---
RUNS_DIR = 'runs/detect/runs/regularize/regularize_run_3' # Ultralytics default save path
# BEST_WEIGHTS_FILENAME = 'best_regularize_3.pt'
# OUTPUT_WEIGHTS_NAME = 'weights_regularize_3.pt'
# # --- SAVE WEIGHTS TO GOOGLE DRIVE ---
# save_weights_to_drive(RUNS_DIR, DRIVE_MODEL_DIR, BEST_WEIGHTS_FILENAME, OUTPUT_WEIGHTS_NAME)
InĀ [Ā ]:
# Run 3
visualize_training_results(RUNS_DIR)
============================================================ š DETAILED TRAINING STATISTICS ============================================================ Total Epochs: 140 šÆ Best mAP50: 0.9201 (Epoch 109) šÆ Best mAP50-95: 0.6715 (Epoch 109) š Final Metrics: mAP50: 0.9008 mAP50-95: 0.6524 Precision: 0.8648 Recall: 0.8667 ============================================================
The metrics show we have successfully reached a good level of generalization, but we are now struggling with specificity (too many false positives), although we are more worried about false negatives. The model is seeing background elements (decorations, trees, lights, people) and wrongly labeling them as Santa.
Flase negatives. These errors reduce the Recall. The model is missing a few true Santa instances. It successfully finds most of actual Santas (Recall = 81%), but precision (0.7725) is moderata, the model is wrong almost about every 5. time when it thinks it found Santa.
InĀ [Ā ]:
# On a TEST SET
# Run validation on the test split
model = YOLO('./yolov8_results/best_regularize_3.pt')
metrics = model.val(data=dataset_yaml, split='test')
print(f"Test results saved to: {metrics.save_dir}")
# Precision, recall, and mAP results
precision = metrics.results_dict['metrics/precision(B)']
recall = metrics.results_dict['metrics/recall(B)']
mAP50 = metrics.results_dict['metrics/mAP50(B)']
mAP50_95 = metrics.results_dict['metrics/mAP50-95(B)']
print(f"Precision: {precision:.2f}")
print(f"Recall: {recall:.2f}")
print(f"mAP50: {mAP50:.2f}")
print(f"mAP50-95: {mAP50_95:.2f}")
Ultralytics 8.3.252 š Python-3.12.12 torch-2.9.0+cu126 CUDA:0 (NVIDIA A100-SXM4-40GB, 40507MiB) Model summary (fused): 72 layers, 3,005,843 parameters, 0 gradients, 8.1 GFLOPs val: Fast image access ā (ping: 0.6Ā±0.2 ms, read: 0.0Ā±0.0 MB/s, size: 31.2 KB) val: Scanning /content/drive/.shortcut-targets-by-id/1DgLXJnKkWgH1Se3L7wSkYBuXljrUEqpi/MC/Santa-10/test/labels... 95 images, 42 backgrounds, 0 corrupt: 100% āāāāāāāāāāāā 95/95 5.3it/s 17.8s val: New cache created: /content/drive/.shortcut-targets-by-id/1DgLXJnKkWgH1Se3L7wSkYBuXljrUEqpi/MC/Santa-10/test/labels.cache Class Images Instances Box(P R mAP50 mAP50-95): 100% āāāāāāāāāāāā 6/6 2.9it/s 2.1s all 95 53 0.926 0.811 0.901 0.677 Speed: 1.0ms preprocess, 4.2ms inference, 0.0ms loss, 2.3ms postprocess per image Results saved to /content/drive/.shortcut-targets-by-id/1DgLXJnKkWgH1Se3L7wSkYBuXljrUEqpi/MC/runs/detect/val5 Test results saved to: /content/drive/.shortcut-targets-by-id/1DgLXJnKkWgH1Se3L7wSkYBuXljrUEqpi/MC/runs/detect/val5 Precision: 0.93 Recall: 0.81 mAP50: 0.90 mAP50-95: 0.68
InĀ [Ā ]:
RUNS_DIR = 'runs/detect/val5'
from IPython.display import display, Image
# F1 curve
f1_path = os.path.join(RUNS_DIR, 'BoxF1_curve.png')
display(Image(filename=f1_path, width=600))
# Precision Recall curve
pr_path = os.path.join(RUNS_DIR, 'BoxPR_curve.png')
display(Image(filename=pr_path, width=600))
# Confusion matrix
cm_path = os.path.join(RUNS_DIR, 'confusion_matrix.png')
display(Image(filename=cm_path, width=600))
The curve for the current run is exceptionally strong, maintaining near-perfect Precision (1.0) until Recall reaches approximately 0.7. The flat line at 1.0 Precision means the model is only making "high-certainty" predictions. It has perfectly memorized the most common Santa features, so it is 100% rightābut only for the "easy" 70% of our data (over-specialization).
If we look at your Overfitting Check plot from the training set above, we can see the validation loss (red) starting to trend upward while the training loss (blue) continues to dive. This onfirms that the model is performing significantly better on its memorized training data than on the validation Santas.
The area under the curve indicates an mAP@0.5 of 0.901. While slightly lower than the 0.927 seen in a previous iteration, it remains highly competitive.
However, we see that now 7/53 Santas were not recognized, which indicates that for our purpose the model is worse.
InĀ [Ā ]:
show_image_results(RUNS_DIR, 2)
print()
show_image_results(RUNS_DIR, 0)
In the last row we see, that the model has picked up on patterns well, as it identified also a Santa that is not annotated in the dataset (image '156_Santa_jpg'), but as it is under the confidence threshold, it still performs worse thatn the S model.
InĀ [3]:
!jupyter nbconvert --to html '/content/drive/MyDrive/FHNW/HS_25/DLBS/minichallenge_hs25_object_detection/MC/03_model_regularization.ipynb.ipynb'
[NbConvertApp] WARNING | pattern '/content/drive/MyDrive/FHNW/HS_25/DLBS/minichallenge_hs25_object_detection/MC/03_model_regularization.ipynb.ipynb' matched no files
This application is used to convert notebook files (*.ipynb)
to various other formats.
WARNING: THE COMMANDLINE INTERFACE MAY CHANGE IN FUTURE RELEASES.
Options
=======
The options below are convenience aliases to configurable class-options,
as listed in the "Equivalent to" description-line of the aliases.
To see all configurable class-options for some <cmd>, use:
<cmd> --help-all
--debug
set log level to logging.DEBUG (maximize logging output)
Equivalent to: [--Application.log_level=10]
--show-config
Show the application's configuration (human-readable format)
Equivalent to: [--Application.show_config=True]
--show-config-json
Show the application's configuration (json format)
Equivalent to: [--Application.show_config_json=True]
--generate-config
generate default config file
Equivalent to: [--JupyterApp.generate_config=True]
-y
Answer yes to any questions instead of prompting.
Equivalent to: [--JupyterApp.answer_yes=True]
--execute
Execute the notebook prior to export.
Equivalent to: [--ExecutePreprocessor.enabled=True]
--allow-errors
Continue notebook execution even if one of the cells throws an error and include the error message in the cell output (the default behaviour is to abort conversion). This flag is only relevant if '--execute' was specified, too.
Equivalent to: [--ExecutePreprocessor.allow_errors=True]
--stdin
read a single notebook file from stdin. Write the resulting notebook with default basename 'notebook.*'
Equivalent to: [--NbConvertApp.from_stdin=True]
--stdout
Write notebook output to stdout instead of files.
Equivalent to: [--NbConvertApp.writer_class=StdoutWriter]
--inplace
Run nbconvert in place, overwriting the existing notebook (only
relevant when converting to notebook format)
Equivalent to: [--NbConvertApp.use_output_suffix=False --NbConvertApp.export_format=notebook --FilesWriter.build_directory=]
--clear-output
Clear output of current file and save in place,
overwriting the existing notebook.
Equivalent to: [--NbConvertApp.use_output_suffix=False --NbConvertApp.export_format=notebook --FilesWriter.build_directory= --ClearOutputPreprocessor.enabled=True]
--coalesce-streams
Coalesce consecutive stdout and stderr outputs into one stream (within each cell).
Equivalent to: [--NbConvertApp.use_output_suffix=False --NbConvertApp.export_format=notebook --FilesWriter.build_directory= --CoalesceStreamsPreprocessor.enabled=True]
--no-prompt
Exclude input and output prompts from converted document.
Equivalent to: [--TemplateExporter.exclude_input_prompt=True --TemplateExporter.exclude_output_prompt=True]
--no-input
Exclude input cells and output prompts from converted document.
This mode is ideal for generating code-free reports.
Equivalent to: [--TemplateExporter.exclude_output_prompt=True --TemplateExporter.exclude_input=True --TemplateExporter.exclude_input_prompt=True]
--allow-chromium-download
Whether to allow downloading chromium if no suitable version is found on the system.
Equivalent to: [--WebPDFExporter.allow_chromium_download=True]
--disable-chromium-sandbox
Disable chromium security sandbox when converting to PDF..
Equivalent to: [--WebPDFExporter.disable_sandbox=True]
--show-input
Shows code input. This flag is only useful for dejavu users.
Equivalent to: [--TemplateExporter.exclude_input=False]
--embed-images
Embed the images as base64 dataurls in the output. This flag is only useful for the HTML/WebPDF/Slides exports.
Equivalent to: [--HTMLExporter.embed_images=True]
--sanitize-html
Whether the HTML in Markdown cells and cell outputs should be sanitized..
Equivalent to: [--HTMLExporter.sanitize_html=True]
--log-level=<Enum>
Set the log level by value or name.
Choices: any of [0, 10, 20, 30, 40, 50, 'DEBUG', 'INFO', 'WARN', 'ERROR', 'CRITICAL']
Default: 30
Equivalent to: [--Application.log_level]
--config=<Unicode>
Full path of a config file.
Default: ''
Equivalent to: [--JupyterApp.config_file]
--to=<Unicode>
The export format to be used, either one of the built-in formats
['asciidoc', 'custom', 'html', 'latex', 'markdown', 'notebook', 'pdf', 'python', 'qtpdf', 'qtpng', 'rst', 'script', 'slides', 'webpdf']
or a dotted object name that represents the import path for an
``Exporter`` class
Default: ''
Equivalent to: [--NbConvertApp.export_format]
--template=<Unicode>
Name of the template to use
Default: ''
Equivalent to: [--TemplateExporter.template_name]
--template-file=<Unicode>
Name of the template file to use
Default: None
Equivalent to: [--TemplateExporter.template_file]
--theme=<Unicode>
Template specific theme(e.g. the name of a JupyterLab CSS theme distributed
as prebuilt extension for the lab template)
Default: 'light'
Equivalent to: [--HTMLExporter.theme]
--sanitize_html=<Bool>
Whether the HTML in Markdown cells and cell outputs should be sanitized.This
should be set to True by nbviewer or similar tools.
Default: False
Equivalent to: [--HTMLExporter.sanitize_html]
--writer=<DottedObjectName>
Writer class used to write the
results of the conversion
Default: 'FilesWriter'
Equivalent to: [--NbConvertApp.writer_class]
--post=<DottedOrNone>
PostProcessor class used to write the
results of the conversion
Default: ''
Equivalent to: [--NbConvertApp.postprocessor_class]
--output=<Unicode>
Overwrite base name use for output files.
Supports pattern replacements '{notebook_name}'.
Default: '{notebook_name}'
Equivalent to: [--NbConvertApp.output_base]
--output-dir=<Unicode>
Directory to write output(s) to. Defaults
to output to the directory of each notebook. To recover
previous default behaviour (outputting to the current
working directory) use . as the flag value.
Default: ''
Equivalent to: [--FilesWriter.build_directory]
--reveal-prefix=<Unicode>
The URL prefix for reveal.js (version 3.x).
This defaults to the reveal CDN, but can be any url pointing to a copy
of reveal.js.
For speaker notes to work, this must be a relative path to a local
copy of reveal.js: e.g., "reveal.js".
If a relative path is given, it must be a subdirectory of the
current directory (from which the server is run).
See the usage documentation
(https://nbconvert.readthedocs.io/en/latest/usage.html#reveal-js-html-slideshow)
for more details.
Default: ''
Equivalent to: [--SlidesExporter.reveal_url_prefix]
--nbformat=<Enum>
The nbformat version to write.
Use this to downgrade notebooks.
Choices: any of [1, 2, 3, 4]
Default: 4
Equivalent to: [--NotebookExporter.nbformat_version]
Examples
--------
The simplest way to use nbconvert is
> jupyter nbconvert mynotebook.ipynb --to html
Options include ['asciidoc', 'custom', 'html', 'latex', 'markdown', 'notebook', 'pdf', 'python', 'qtpdf', 'qtpng', 'rst', 'script', 'slides', 'webpdf'].
> jupyter nbconvert --to latex mynotebook.ipynb
Both HTML and LaTeX support multiple output templates. LaTeX includes
'base', 'article' and 'report'. HTML includes 'basic', 'lab' and
'classic'. You can specify the flavor of the format used.
> jupyter nbconvert --to html --template lab mynotebook.ipynb
You can also pipe the output to stdout, rather than a file
> jupyter nbconvert mynotebook.ipynb --stdout
PDF is generated via latex
> jupyter nbconvert mynotebook.ipynb --to pdf
You can get (and serve) a Reveal.js-powered slideshow
> jupyter nbconvert myslides.ipynb --to slides --post serve
Multiple notebooks can be given at the command line in a couple of
different ways:
> jupyter nbconvert notebook*.ipynb
> jupyter nbconvert notebook1.ipynb notebook2.ipynb
or you can specify the notebooks list in a config file, containing::
c.NbConvertApp.notebooks = ["my_notebook.ipynb"]
> jupyter nbconvert --config mycfg.py
To see all available configurables, use `--help-all`.